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Reactive Learning Agents

Presented at the Google Developer Group NYC Meetup on 9/12/16 http://www.meetup.com/gdgnyc/events/231371634/

This talk is focused on the design of learning agents using the techniques of reactive machine learning. We explored the difference between software agents (bots), intelligent agents, and learning agents. As the most complex class of agent, a learning agent has a sophisticated internal architecture, which we broke down into different capabilities. Finally, we examined how the techniques from reactive machine learning can allow us to build learning capabilities into our agents.

This talk is not just be about AI concepts, though. It covers a range of pragmatic techniques to aid you in your efforts to implement learning agents. Throughout, we considered where and how we can use external resources like libraries, services, datasets, and even humans to solve sub-problems in the agent design process.

81.
Dialyzer in Action
ml_system.ex:3: Function predict/1 has no local return
ml_system.ex:6: The call
'Elixir.MLSystem':call_model_b(feature@1::number()) will never
return since it differs in the 1st argument from the success
typing arguments: (binary())
ml_system.ex:22: Invalid type specification for function
'Elixir.MLSystem':call_model_b/1. The success typing is
(binary()) -> binary()
ml_system.ex:23: Function call_model_b/1 has no local return

82.
Dialyzer in Action
ml_system.ex:24: The call
'Elixir.String':upcase(feature@1::number()) will never return
since the success typing is (binary()) -> bitstring() and the
contract is (t()) -> t()
ml_system.ex:38: Function ensemble/1 will never be called